Segmentation of the Cerebrospinal Fluid from MRI Images for the Treatment of Disc Herniations

About 80 percent of people are affected at some point in their lives by lower back pain, which is one of the most common neurological diseases and reasons for long-term disability in the United States. The symptoms are primarily caused by overly heavy lifting and/or overstretching of the back, leading to a rupture and an outward bulge of an intervertebral disc, which puts pressure on and pinches the nerve fibers of the spine. The most common form is a lumbar disc herniation between the fourth and fifth lumbar vertebra and between the fifth lumbar vertebra and the sacrum. In recent years the diagnosis of lower back pain has improved, mainly due to enhanced imaging techniques and imaging quality, but the surgical therapy remains hazardous. Reasons for this include low visibility when accessing the lumbar area and the high risk of causing permanent damage when touching the nerve fibers. A new approach for increasing patient safety is the segmentation and visualization of the cerebrospinal fluid in the lower lumbar region of the vertebral column. For this purpose a new fully-automatic and a semi-automatic approach were developed for separating the cerebrospinal fluid from its surroundings on T2-weighted MRI scans of the lumbar vertebra. While the fully-automatic algorithm is realized by a model-based searching method and a volume-based segmentation, the semi-automatic algorithm requires a seed point and performs the segmentation on individual axial planes through a combination of a region-based segmentation algorithm and a thresholding filter. Both algorithms have been applied to four T2-weighted MRI datasets and are compared with a gold-standard segmentation. The segmentation overlap with the gold-standard was 78.7 percent for the fully-automatic algorithm and 93.1 percent for the semi-automatic algorithm. In the pathological region the fully-automatic algorithm obtained a similarity of 56.6 percent, compared to 87.8 percent for the semi-automatic algorithm

Regularizing Deep Models for Visual Recognition

Image understanding is a shared goal in all computer vision problems. This objective includes decomposing the image into a set of primitive components through which one can perform region segmentation, region labeling, object recognition and finally modeling the interactions between recognized objects. However, due to the large intra-class variations in appearance, shape and structure, extracting image primitives is highly challenging. While images come in the form of intensity matrices, in order to cope with this large variations, a high-level abstraction of images is required. Therefore, the main challenge is to bridge the gap between the low-level pixel representation and the high-level abstract image descriptors. In recent years, we have witnessed a striking popularity of the learned image descriptors using deep networks for visual recognition. The multi-layer architecture of these networks is particularly useful in capturing the hierarchical structure of the image data: simple features are detected at lower layers and fed into higher layers for extracting more complex and abstract representations. Despite the remarkable representational power of deep networks, training these models is computationally expensive. In addition, considering the lack of enough labeled training data in many applications, over-fitting is a serious threat for deep models with large number of free parameters. Also, there are innate issues with the gradient-based optimization procedure used for parameter learning in these models. This research is aimed at addressing the above issues by leveraging domain knowledge. Particularly, we focus on tailoring deep networks for visual recognition through exploiting the characteristics of the image data. These modifications tend to regularize deep models and therefore, improve their generalization performance. We propose novel ways for incorporation of image-specific domain knowledge into deep networks. As part of this thesis, we show how one can significantly decrease the number of free parameters in fully-connected architectures by exploiting the global characteristics of the image data. For convolutional networks, a new multi-neighborhood architecture is introduced which can capture scale-dependent features. In this architecture, the fine-scale image structures (i.e., appearance features) are captured using a small-sized neighborhood while coarse-scale characteristics (i.e., shape features) are detected by considering a wider range area around each pixel. Besides, we propose an effective regularization method for deep networks in which a frequency parameter is devised to specifically treat the issues of gradient-based optimization for training these models. Finally, we introduce a stage-wise training framework for deep networks in which the learning process is broken down into a number of related sub-tasks completed stage-by-stage, where the learned parameters at each stage acts as a prior for the next stage. This goal is achieved through "gradual" injection of the information presented in the training data so that in the early stages of training, the "coarse-scale" properties of the data are captured while the "finer-scale" characteristics are learned in later stages. The performance of the proposed methods are assessed on a number of image classification data sets. Our comprehensive empirical analysis demonstrates that these "regularized" networks offer a better discrimination and generalization performance compared to their domain-oblivious counterparts